Problems loading textual data with scikit-learn?

Question

I'm using my own data to classify into two categories some data, so let:

from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

# Load the text data
categories = [
    'CLASS_1',
    'CLASS_2',
]

text_train_subset = load_files('train',
    categories=categories)

text_test_subset = load_files('test',
    categories=categories)

# Turn the text documents into vectors of word frequencies
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(text_train_subset)
y_train = text_train_subset.target


classifier = MultinomialNB().fit(X_train, y_train)
print("Training score: {0:.1f}%".format(
    classifier.score(X_train, y_train) * 100))

# Evaluate the classifier on the testing set
X_test = vectorizer.transform(text_test_subset.data)
y_test = text_test_subset.target
print("Testing score: {0:.1f}%".format(
    classifier.score(X_test, y_test) * 100))

For the above code and the documentation , I have the following directory schema:

data_folder/

    train_folder/
        CLASS_1.txt CLASS_2.txt
    test_folder/
        test.txt

Then I get this error:

    % (size, n_samples))
ValueError: Found array with dim 0. Expected 5

I also tried fit_transform but still the same. How can I solve this dimession problem?

Answer 1

The first problem is you've got the wrong directory structure. You need it to be like

container_folder/
    CLASS_1_folder/
        file_1.txt, file_2.txt ... 
    CLASS_2_folder/
        file_1.txt, file_2.txt, ....

You need to have both the train and test set in this directory structure. Alternatively, you can have all data in one directory and use train_test_split to split it in two.

Secondly,

X_train = vectorizer.fit_transform(text_train_subset)

needs to be

X_train = vectorizer.fit_transform(text_train_subset.data) # added .data

Here is a complete and working example:

from sklearn.datasets import load_files
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB

text_train_subset = load_files('sample-data/web')
text_test_subset = text_train_subset # load your actual test data here

# Turn the text documents into vectors of word frequencies
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(text_train_subset.data)
y_train = text_train_subset.target


classifier = MultinomialNB().fit(X_train, y_train)
print("Training score: {0:.1f}%".format(
    classifier.score(X_train, y_train) * 100))

# Evaluate the classifier on the testing set
X_test = vectorizer.transform(text_test_subset.data)
y_test = text_test_subset.target
print("Testing score: {0:.1f}%".format(
    classifier.score(X_test, y_test) * 100))

The directory structure of sample-data/web is

sample-data/web
├── de
│   ├── apollo8.txt
│   ├── fiv.txt
│   ├── habichtsadler.txt
└── en
    ├── elizabeth_needham.txt
    ├── equipartition_theorem.txt
    ├── sunderland_echo.txt
    └── thespis.txt